Efficacy Analysis in Different Architectures of Convolutional Neural Networks for COVID-19 Diagnosis Based on X-Ray Images-Reference-Cited by-同舟云学术

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Efficacy Analysis in Different Architectures of Convolutional Neural Networks for COVID-19 Diagnosis Based on X-Ray Images

Published:2023-11-22 Issue: Volume: Page:74-80
ISSN:
Container-title:2023 Fourth International Conference on Information Systems and Software Technologies (ICI2ST)
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Author:

Paspuel-Montalvo Emilio¹,Valencia-Cevallos Camila¹,Guerrero-Ligña Alejandra¹,Villalba-Meneses Fernando¹,Cadena-Morejon Carolina²,Terán-Grijalva Cristhian³,Arévalo-Serrano Gabriela¹,Tirado-Espín Andrés²,Almeida-Galárraga Diego¹

Affiliation:

1. School of Biological Sciences and Engineering, Universidad Yachay Tech,Urcuquí,Ecuador

2. School of Mathematical and Computational Sciences, Universidad Yachay Tech,Urcuquí,Ecuador

3. Grupo de Fuerzas Especiales No 27, “Grad. Miguel Iturralde”, Ejercito Ecuatoriano,Latacunga,Ecuador

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10604454/10604445/10604592.pdf?arnumber=10604592

Reference45 articles.

1. Characteristics of SARS-CoV-2 and COVID-19

2. OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

3. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT

4. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

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